Analysis of the opinions of individuals on the COVID-19 vaccination on social media.

The COVID-19 pandemic continues to threaten public health globally. To develop effective interventions and campaigns to raise vaccination rates, policy makers need to understand people's attitudes towards vaccination. We examine the perspectives of people in India, the United States, Canada, and the United Kingdom on the administration of different COVID-19 vaccines. We analyse how public opinion and emotional tendencies regarding the COVID-19 vaccines relate to popular issues on social media. We employ machine learning algorithms to forecast thoughts based on the social media posts. The prevailing emotional tendency indicates that individuals have faith in immunisation. However, there is a likelihood that significant statements or events on a national, international, or political scale influence public perception of vaccinations. We show how public health officials can track public attitudes and opinions towards vaccine-related information in a geo-aware manner, respond to the sceptics, and increase the level of vaccine trust in a particular region or community.

Machine learning based attribution mapping of climate related discussions on social media.

A united front from all the stakeholders including public, administration and academia alike is required to counter the growing threat of climate change. The recent rise of social media as the new public address system, makes it an ideal source of information to assess public discussions and responses in real time. We mine c.1.7 m posts from 55 climate related subreddits on social media platform Reddit since its inception. Using USE, a state-of-the-art sentence encoder, and K-means clustering algorithm, we develop a machine learning based approach to identify, store, process and classify the posts automatically, and at a scale. In the broad and multifaceted theme of climate change, our approach narrows down the focus to 10 critical underlying themes comprising the public discussions on social media over time. Furthermore, we employ a full order partial correlation analysis to assess the relationship between the different identified themes. We show that in line with Paris Agreement, while the climate science community has been successful in influencing the discussions on both the causes and effects of climate change, the public administration has failed to appropriately communicate the causes of climate change and has been able to influence only the discussions on the effects of it. Hence, our study shows a clear gap in the public communication by the administration, wherein counter-intuitively less emphasis has been given on the drivers of climate change. This information can be particularly beneficial to policymakers and climate activists in decision making as they try to close the gap between public and academia.

COVID-19 seroprevalence after the first UK wave of the pandemic and its association with the physical and mental wellbeing of secondary care healthcare workers.

Objectives: To determine the seroprevalence of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) antibody status amongst healthcare workers (HCWs) working through the first wave of the Coronavirus (COVID-19) pandemic in 2020. To examine the association of seroprevalence and self-reported COVID-19 symptoms with occupation, sex, and ethnicity; and how these factors were associated with physical and mental wellbeing. Design: Single-centre cohort study. Setting: Large public hospital in the United Kingdom. Intervention: All HCWs who had been tested for anti-SARS-CoV-2 immunoglobulin (Ig) G nucleocapsid antibody in summer 2020 were asked to complete an electronic survey focusing on their physical and mental health in Winter 2020-21. This survey was comprised of the Short Form 12v2, Physical Component Summary (PCS), Mental Component Summary (MCS), and Generalised Anxiety Disorder 7-item (GAD-7) questionnaires. Results: 7604/9781 (77.7%) HCWs were antibody tested, of which 1082 completed the full survey. Antibody testing was conducted between 17/06/20-30/07/20, during which time our seroprevalence rate was 28% (299/1082). Of those self-reporting COVID-19 symptoms, 51% (201/395) were antibody positive. Antibody-positive participants had lower PCS scores (p = 0.016), indicating poorer physical health. Lower PCS scores were also found in those deemed high risk for COVID-19 by their GP (p = 0.001), and those aged >44 years (p = 0.009). Antibody-negative participants had lower MCS scores (p = 0.044), indicating poorer mental health. Those who self-reported COVID-19 symptoms had lower PCS scores (p=<0.001) than those with no symptoms. Lower MCS scores were found in women (p = 0.001), Caucasians (p = 0.018), non-clinicians (p = 0.001), and those aged <44 years (p = 0.009). Significantly higher GAD-7 anxiety scores were evident in staff aged <44 years (p = 0.023), and those with self-reported COVID symptoms (p = 0.031). Doctors had lower GAD-7 anxiety scores (p = 0.009). Conclusion: Self-reported symptoms did not correlate with seroprevalence; data surrounding this can be useful for future workforce planning. Interventions are needed to reduce the mental and physical burden of the pandemic on HCWs. Further work is needed to identify which particular HCWs may require further support, to ensure well-being and effective patient care. Trial registration: Sponsor Protocol number - 2020COV112, Clinicaltrials.gov number -NCT04527432.

Using GAM functions and Markov-Switching models in an evaluation framework to assess countries' performance in controlling the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most "at a glance" comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socio-economic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series. METHODS: Generalized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University. RESULTS: Our methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people's diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths. CONCLUSIONS: The new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world.

The UPTAKE study: a cross-sectional survey examining the insights and beliefs of the UK population on COVID-19 vaccine uptake and hesitancy.

OBJECTIVE: A key challenge towards a successful COVID-19 vaccine uptake is vaccine hesitancy. We examine and provide novel insights on the key drivers and barriers towards COVID-19 vaccine uptake. DESIGN: This study involved an anonymous cross-sectional online survey circulated across the UK in September 2020. The survey was designed to include several sections to collect demographic data and responses on (1) extent of agreement regarding various statements about COVID-19 and vaccinations, (2) previous vaccination habits (eg, if they had previously declined vaccination) and (3) interest in participation in vaccine trials. Multinominal logistic models examined demographic factors that may impact vaccine uptake. We used principle component analysis and text mining to explore perception related to vaccine uptake. SETTING: The survey was circulated through various media, including posts on social media networks (Facebook, Twitter, LinkedIn and Instagram), national radio, news articles, Clinical Research Network website and newsletter, and through 150 West Midlands general practices via a text messaging service. PARTICIPANTS: There were a total of 4884 respondents of which 9.44% were black, Asian and minority ethnic (BAME) group. The majority were women (n=3416, 69.9%) and of white ethnicity (n=4127, 84.5%). RESULTS: Regarding respondents, overall, 3873 (79.3%) were interested in taking approved COVID-19 vaccines, while 677 (13.9%) were unsure, and 334 (6.8%) would not take a vaccine. Participants aged over 70 years old (OR=4.63) and the BAME community (OR=5.48) were more likely to take an approved vaccine. Smokers (OR=0.45) and respondents with no known illness (OR=0.70) were less likely to accept approved vaccines. The study identified 16 key reasons for not accepting approved vaccines, the most common (60%) being the possibility of the COVID-19 vaccine having side effects. CONCLUSIONS: This study provides an insight into focusing on specific populations to reduce vaccine hesitancy. This proves crucial in managing the COVID-19 pandemic.

The UPTAKE study: implications for the future of COVID-19 vaccination trial recruitment in UK and beyond.

BACKGROUND: Developing a safe and effective vaccine will be the principal way of controlling the COVID-19 pandemic. However, current COVID-19 vaccination trials are not adequately representing a diverse participant population in terms of age, ethnicity and comorbidities. Achieving the representative recruitment targets that are adequately powered to the study remains one of the greatest challenges in clinical trial management. To ensure accuracy and generalisability of the safety and efficacy conclusions generated by clinical trials, it is crucial to recruit patient cohorts as representative as possible of the future target population. Missing these targets can lead to reduced validity of the study results and can often slow down drug development leading to costly delays. OBJECTIVE: This study explores the key factors related to perceptions and participation in vaccination trials. METHODS: This study involved an anonymous cross-sectional online survey circulated across the UK. Statistical analysis was done in six phases. Multi-nominal logistic models examined demographic and geographic factors that may impact vaccine uptake. RESULTS: The survey had 4884 participants of which 9.44% were Black Asian Minority Ethnic (BAME). Overall, 2020 (41.4%) respondents were interested in participating in vaccine trials; 27.6% of the respondents were not interested and 31.1% were unsure. The most interested groups were male (OR = 1.29), graduates (OR = 1.28), the 40-49 and 50-59 age groups (OR = 1.88 and OR = 1.46 respectively) and those with no health issues (OR = 1.06). The least interested groups were BAME (OR = 0.43), those from villages and small towns (OR = 0.66 and 0.54 respectively) and those aged 70 and above (OR = 1.11). CONCLUSIONS: In order to have a vaccination that is generalisable to the entire population, greater work needs to be done in engaging a diverse cohort of participants. Public health campaigns need to be targeted in improving trial recruitment rates for the elderly, BAME community and the less educated rural population.